Sparse CSEM inversion driven by seismic coherence

Marine controlled source electromagnetic (CSEM) data inversion for hydrocarbon exploration is often challenging due to high computational cost, physical memory requirement and low resolution of the obtained resistivity map. This paper aims to enhance both the speed and resolution of CSEM inversion by introducing structural geological information in the inversion algorithm. A coarse mesh is generated for Occam's inversion, where the parameters are fewer than in the fine regular mesh. This sparse mesh is defined as a coherence-based irregular (IC) sparse mesh, which is based on vertices extracted from available geological information. Inversion results on synthetic data illustrate that the IC sparse mesh has a smaller inversion computational cost compared to the regular dense (RD) mesh. It also has a higher resolution than with a regular sparse (RS) mesh for the same number of estimated parameters. In order to study how the IC sparse mesh reduces the computational time, four different meshes are generated for Occam's inversion. As a result, an IC sparse mesh can reduce the computational cost while it keeps the resolution as good as a fine regular mesh. The IC sparse mesh reduces the computational cost of the matrix operation for model updates. When the number of estimated parameters reduces to a limited value, the computational cost is independent of the number of parameters. For a testing model with two resistive layers, the inversion result using an IC sparse mesh has higher resolution in both horizontal and vertical directions. Overall, the model representing significant geological information in the IC mesh can improve the resolution of the resistivity models obtained from inversion of CSEM data.

[1]  Rachid Deriche,et al.  Using geometric corners to build a 2D mosaic from a set of images , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Manuel Amaya,et al.  Efficient computation of approximate low-rank Hessian for 3D CSEM inversion , 2014 .

[3]  I. Brevik,et al.  Investigating the exploration potential for 3D CSEM using a calibration survey over the Troll Field , 2009 .

[4]  François Guibault,et al.  Two-dimensional metric tensor visualization using pseudo-meshes , 2006, Engineering with Computers.

[5]  A. Michelini.  An adaptive-grid formalism for traveltime tomography , 1995 .

[6]  Kerry Key,et al.  Seismically regularized controlled-source electromagnetic inversion , 2012 .

[7]  Nuno V. da Silva,et al.  A finite element multifrontal method for 3D CSEM modeling in the frequency domain , 2012 .

[8]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[9]  Kerry Key,et al.  2D marine controlled-source electromagnetic modeling: Part 1 — An adaptive finite-element algorithm , 2007 .

[10]  U. Kothe,et al.  Integrated edge and junction detection with the boundary tensor , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  Kerry Key,et al.  1D inversion of multicomponent, multifrequency marine CSEM data: Methodology and synthetic studies for resolving thin resistive layers , 2009 .

[13]  G. Böhm,et al.  3D adaptive tomography using Delaunay triangles and Voronoi polygons , 2000 .

[14]  Shigeru Ando,et al.  Image Field Categorization and Edge/Corner Detection from Gradient Covariance , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Michael S. Bahorich,et al.  3-D seismic discontinuity for faults and stratigraphic features; the coherence cube , 1995 .

[16]  R. Parker,et al.  Occam's inversion; a practical algorithm for generating smooth models from electromagnetic sounding data , 1987 .

[17]  Aldo Vesnaver,et al.  Staggered or adapted grids for seismic tomography , 2000 .

[18]  Jeffrey S. Ovall,et al.  A parallel goal-oriented adaptive finite element method for 2.5-D electromagnetic modelling , 2011 .

[19]  Kerry Key,et al.  Marine EM Inversion Using Unstructured Grids: A 2D Parallel Adaptive Finite Element Algorithm , 2012 .

[20]  Michael Commer,et al.  New advances in three‐dimensional controlled‐source electromagnetic inversion , 2007 .

[21]  L. Sambuelli,et al.  Staggered grid inversion of cross hole 2-D resistivity tomography , 2014 .

[22]  Farzin Mokhtarian,et al.  Performance evaluation of corner detectors using consistency and accuracy measures , 2006, Comput. Vis. Image Underst..

[23]  D. Hale,et al.  Image-guided sparse-model full waveform inversion , 2012 .